perform.singleR.annotation: Performs automated cell annotation on query datasets using...

In connorhknight/IBRAP_no_decontX: Integrated Benchmarking scRNA-seq Analytical Pipeline

Description

SingleR iterates through singular cells and iterates through probabilitiy comparisons to identify which cell type the query cell is likely to be. If a probably cell type cannot be discovered then

Usage

perform.singleR.annotation(
  object,
  assay = "RAW",
  slot = "counts",
  ref,
  log.transform.query = TRUE,
  tpm.transform.query = FALSE,
  log.transform.ref = TRUE,
  tpm.transform.ref = FALSE,
  ref.labels,
  column.suffix = "1",
  ...
)

Arguments

object	IBRAP S4 class object
assay	Character. String containing indicating which assay to use
slot	Character. String indicating which slot within the assay should be sourced
ref	AnyMatrix. A matrix of the reference datasets, if data is end-bias then it should be log normalised, if it is full-length then it requires tpm normalisation. Both can be completed within this function.
ref.labels	Vector. The cluster assignments for the reference data. Default = NULL
column.suffix	Character. A suffix to append the end of the new metadata columns if this functiuons is to be used multiple times. Default = '1'
...	arguments to be passed to singleR::SingleR
log.transform	Boolean. Should the reference data be log transformed. Default = TRUE
tpm.transform	Boolean. Should the reference data be tpm normalised. Default = FALSE

Value

Produces a new 'methods' assay containing normalised, scaled and HVGs.

Examples

object <- perform.singleR.annotation(object = object, ref = reference_matrix, ref.labels = metadata_reference$celltype)

connorhknight/IBRAP_no_decontX documentation built on Feb. 13, 2022, 2:32 p.m.